We extract destination guides, POI metadata, curated itineraries, and travel articles from Lonely Planet. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Destinations objects from lonelyplanet.com. All fields typed and schema-versioned.
"destination_id": "dest-8492", "name": "Kyoto", "hierarchy_level": "city", "country": "Japan", "continent": "Asia", "best_time_to_visit": "March to May, September to November", "currency": "Japanese Yen (JPY)", "languages": "['Japanese']"
| # | destination_id | name | hierarchy_level | parent_region | country | continent |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Points of Interest objects from lonelyplanet.com. All fields typed and schema-versioned.
"poi_id": "poi-91023", "name": "Fushimi Inari-Taisha", "category": "Sights", "subcategory": "Shrines & Temples", "destination": "Kyoto", "latitude": 34.96714, "longitude": 135.77267, "our_pick_badge": true
| # | poi_id | name | category | subcategory | destination | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Itineraries objects from lonelyplanet.com. All fields typed and schema-versioned.
"itinerary_id": "itin-442", "title": "Classic Japan: Tokyo to Kyoto", "duration_days": 14, "transport_type": "Train", "budget_level": "Moderate", "stops_count": 6, "stops_array": "['Tokyo', 'Hakone', 'Takayama', 'Kyoto', 'Nara', 'Osaka']", "total_distance_km": 650
| # | itinerary_id | title | primary_destination | duration_days | author | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Articles objects from lonelyplanet.com. All fields typed and schema-versioned.
"article_id": "art-11942", "title": "10 best things to do in Kyoto", "author": "John Doe", "publish_date": "2025-10-12", "category": "Activities", "destination_tags": "['Kyoto', 'Japan']", "read_time_minutes": 8, "url": "https://www.lonelyplanet.com/articles/best-things-to-do-in-kyoto"
| # | article_id | title | author | publish_date | category | destination_tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Guidebooks objects from lonelyplanet.com. All fields typed and schema-versioned.
"book_id": "bk-994", "title": "Japan Travel Guide", "isbn_13": "9781788680652", "edition": 17, "publication_date": "2023-08-01", "price_usd": 27.99, "format_types": "['Paperback', 'eBook']", "pages": 928
| # | book_id | title | isbn_13 | edition | publication_date | price_usd |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Lonely Planet scraper handles the complete content hierarchy: global destinations, granular POIs, structured itineraries, and editorial content - with JavaScript rendering and anti-bot circumvention built in.
Extract relational data from continent level down to specific neighbourhoods, maintaining parent-child entity relationships.
Capture sights, restaurants, bars, and hotels with granular metadata including opening hours, price tiers, and contact details.
Extract precise latitude and longitude coordinates for every POI and destination for integration into mapping applications.
Convert narrative travel routes into structured JSON arrays detailing day-by-day stops, transport modes, and transit times.
Scrape editorial content, travel tips, and practical advice articles, complete with author attribution and publication dates.
Extract structured data on visa requirements, currency exchange, local transport, and health advisories per country.
Track the Lonely Planet shop for guidebook editions, ISBNs, page counts, and digital format availability.
Extract raw CDN URLs for destination photography, POI images, and guidebook covers without watermarks.
Run recurring pipelines to capture new articles, updated POI operating hours, and seasonal destination advice.
Brief in. Clean data out.
Provide target regions, POI categories, or article tags. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and Next.js state extraction for lonelyplanet.com.
Schema validation, null-rate checks, and geocoordinate formatting verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel publishers use modern SPA frameworks and aggressive CDN caching. Here is how we maintain data integrity.
Lonely Planet pages are rendered via Next.js. Instead of brittle DOM scraping, we intercept and parse the underlying JSON hydration state, ensuring 100% field accuracy and zero missed elements.
Travel data is highly relational. We maintain strict parent-child mappings during extraction so a specific cafe is correctly linked to its neighbourhood, city, region, and country records.
Address strings vary wildly across global destinations. We extract raw latitude/longitude coordinates from embedded map widgets and normalise address fields into structured components.
Publishers use aggressive edge caching. We append cache-busting parameters and rotate request headers to ensure we extract the most recent content updates and POI changes.
We use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass rate limits and Web Application Firewalls.
Online Travel Agencies augment their booking flows with expert POI recommendations and destination descriptions.
LLM developers ingest structured travel guides and itineraries to train specialised trip-planning assistants.
B2C travel apps use curated stops and transit data to auto-generate realistic day-by-day trip schedules.
Real estate and retail analysts map POI density and 'Our Pick' badges to evaluate neighbourhood desirability.
Tourism boards monitor coverage volume and sentiment for their regions against competing global destinations.
Travel portals syndicate practical advice, visa requirements, and health tips to provide comprehensive user resources.
"Lonely Planet holds the definitive structured dataset of global travel knowledge, but turning editorial pages into relational databases requires a dedicated pipeline."
Extracting travel data requires parsing nested Next.js state, normalising inconsistent global address formats, and mapping relational hierarchies from continents down to specific street corners. DataFlirt manages this complexity so your engineering team can focus on building travel products.
Everything supported by our lonelyplanet.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, Next.js hydration, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About lonelyplanet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available travel information and POI metadata is generally permissible. DataFlirt targets only public, non-authenticated content. We do not extract personal data or circumvent authentication walls. Clients should review Lonely Planet's ToS and consult legal counsel for specific use cases.
We intercept the Next.js hydration state embedded in the page source. This allows us to extract clean JSON data directly from the application state rather than relying on brittle CSS selectors that break during UI updates.
Yes. Every POI record includes latitude and longitude values extracted directly from the underlying map data structures, ensuring precise placement for your downstream applications.
Yes. Our pipeline maps the full geographical hierarchy. A restaurant record will include foreign keys or nested objects linking it to its parent neighbourhood, city, region, and country.
Full catalogue refreshes typically complete within a 12-24 hour window depending on scale. We can configure delta pipelines to run weekly or monthly to capture new articles and POI updates.
The Thorn Tree forum is currently archived and read-only. We can extract historical thread data and posts if required for your specific NLP or research use case.
Our smallest packages start at a defined region or country list with one-off delivery. For global datasets or continuous updates, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 POIs or 50 articles as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full global POI dump or continuous updates for new travel articles - we scope, build, and operate the pipeline. Tell us what you need.
